7 papers
Geometry-Calibrated Conformal Abstention for Language Models
Rui Xu, Yi Chen, Sihong Xie +1
When language models lack relevant knowledge for a given query, they frequently generate plausible responses that can be hallucinations, rather than admitting being agnostic about…
CoAct: Co-Active LLM Preference Learning with Human-AI Synergy
Ruiyao Xu, Mihir Parmar, Tiankai Yang +3
Learning from preference-based feedback has become an effective approach for aligning LLMs across diverse tasks. However, high-quality human-annotated preference data remains expen…
No Attacker Needed: Unintentional Cross-User Contamination in Shared-State LLM Agents
Tiankai Yang, Jiate Li, Yi Nian +5
LLM-based agents increasingly operate across repeated sessions, maintaining task states to ensure continuity. In many deployments, a single agent serves multiple users within a tea…
DS-Instruct: Domain-Specific Data Synthesis for Large Language Models Instruction Tuning
Ruiyao Xu, Noelle I. Samia, Han Liu
Adapting Large Language Models (LLMs) to specialized domains requires high-quality instruction tuning datasets, which are expensive to create through human annotation. Existing dat…
GNN-as-Judge: Unleashing the Power of LLMs for Graph Learning with GNN Feedback
Ruiyao Xu, Kaize Ding
Large Language Models (LLMs) have shown strong performance on text-attributed graphs (TAGs) due to their superior semantic understanding ability on textual node features. However,…
AD-LLM: Benchmarking Large Language Models for Anomaly Detection
Tiankai Yang, Yi Nian, Shawn Li +9
Anomaly detection (AD) is an important machine learning task with many real-world uses, including fraud detection, medical diagnosis, and industrial monitoring. Within natural lang…